predict.KFS: Estimate the Missing Observations of a State Space Model

In jrnold/KFAS: Kalman Filter and Smoother for Exponential Family State Space Models.

Description

Function predict.KFS computes the expected values of missing observations given the observed data.

Usage

  ## S3 method for class 'KFS'
 predict(object, fill = FALSE, ...)

Arguments

object	object of class KFS ie. the output from function KFS.
fill	If FALSE, only predictions of missing observations are returned, and other time points are markes as NA. This is convinient for plotting purposes. If TRUE, original time series is filled with predicted values for missing observations, and F[t] = 0 if observation is not missing. Default is FALSE.
...	Ignored.

Value

A list with the following components:

y	Time series object with missing observations replaced by E(y[t]|y).
F	Covariances Cov(y[t]|y). Note that this is the usual multivariate version of F[t] given by Z[t]P[t]Z'[t] + H[t], not the univariate version given by KFS.

jrnold/KFAS documentation built on May 19, 2019, 11:55 p.m.